DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics
Name
fcvm-08-730316.pdf
Description
Published version
Size
3.39 MB
Format
Adobe PDF
Checksum (MD5)
7f42f3ba1f8432909bd3f525109f7533
Author(s)
Morales, Manuel A
van den Boomen, Maaike
Nguyen, Christopher
Kalpathy-Cramer, Jayashree
Rosen, Bruce R
Stultz, Collin M
Izquierdo-Garcia, David
Catana, Ciprian
Date Issued
2021
Journal
Frontiers in Cardiovascular Medicine
Publisher
Frontiers Media SA
Citation
Morales, Manuel A, van den Boomen, Maaike, Nguyen, Christopher, Kalpathy-Cramer, Jayashree, Rosen, Bruce R et al. 2021. "DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics." Frontiers in Cardiovascular Medicine, 8.
Version
Final published version
Abstract
Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data provides a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wider clinical use. We designed and validated a fast, fully-automatic deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data consisting of segmentation and motion estimation convolutional neural networks. The final motion network design, loss function, and associated hyperparameters are the result of a thorough ad hoc implementation that we carefully planned specific for strain quantification, tested, and compared to other potential alternatives. The optimal configuration was trained using healthy and cardiovascular disease (CVD) subjects (n = 150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD test subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 ± 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 10 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was good to excellent (>0.75) for all global measures and most polar map segments. In conclusion, we developed and evaluated the first end-to-end learning-based workflow for automated strain analysis from cine-MRI data to quantitatively characterize cardiac mechanics of healthy and CVD subjects.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.3389/FCVM.2021.730316
